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A platform for research: civil engineering, architecture and urbanism
Regional Landslide Sensitivity Analysis Based on CPSO-LSSVM
Abstract Landslide sensitivity analysis is of great significance for predicting landslide hazards. Taking the landslide in the hilly area of Sichuan Province as an example, through the interpretation of high spatial resolution remote sensing images and the analysis of the occurrence mechanism of landslides in the low hilly areas of Sichuan Province, eight landslide susceptibility evaluation factors were obtained. (elevation, slope, terrain relief, rivers, roads, geotechnical types, NDVI, fault structures). Then, using the neighborhood statistical analysis, ArcGIS technology and other methods to obtain training sample data and regional sample data. According to the characteristics of the landslide development, the Chao Particle Swarm Optimization (cpso) is used to optimize the parameters of the Least Square Support Vector Machine (lssvm), the cpso-lssvm landslide sensitivity prediction model was formed. The experimental results show that cpso-lssvm has obtained good prediction results in landslide sensitivity evaluation, and the prediction accuracy has increased to 70.5%.
Regional Landslide Sensitivity Analysis Based on CPSO-LSSVM
Abstract Landslide sensitivity analysis is of great significance for predicting landslide hazards. Taking the landslide in the hilly area of Sichuan Province as an example, through the interpretation of high spatial resolution remote sensing images and the analysis of the occurrence mechanism of landslides in the low hilly areas of Sichuan Province, eight landslide susceptibility evaluation factors were obtained. (elevation, slope, terrain relief, rivers, roads, geotechnical types, NDVI, fault structures). Then, using the neighborhood statistical analysis, ArcGIS technology and other methods to obtain training sample data and regional sample data. According to the characteristics of the landslide development, the Chao Particle Swarm Optimization (cpso) is used to optimize the parameters of the Least Square Support Vector Machine (lssvm), the cpso-lssvm landslide sensitivity prediction model was formed. The experimental results show that cpso-lssvm has obtained good prediction results in landslide sensitivity evaluation, and the prediction accuracy has increased to 70.5%.
Regional Landslide Sensitivity Analysis Based on CPSO-LSSVM
Li, Yanze (author) / Yang, Zhenjian (author) / Zhang, Yunjie (author) / Jin, Zhou (author)
2019-01-01
11 pages
Article/Chapter (Book)
Electronic Resource
English
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